A tax is generally evaluated on two criteria: efficiency and equity. The more “deadweight loss” the tax causes, the more inefficient it is. Deadweight loss results from people changing their behavior in response to the tax and, in principle, can be calculated as a welfare loss.

Closely related to efficiency is how the tax affects policy goals. This question is particularly relevant here because the service being taxed is precisely the service the tax is also suppose to support, making it possible that the tax itself could undo any benefits of the spending it funds.

Equity—in general, how much people of different income levels pay—is simple in concept but difficult in practice since it is not possible to say what the “right” share of the tax any given group should pay.

Perhaps surprisingly to some, a broadband tax may actually be efficient relative to some other options, including income taxes (i.e., coming from general revenues). Historically, universal service funds were raised by taxes on long distance service, which is highly price sensitive, making the tax quite inefficient.

By contrast, for the typical household, fixed (and, increasingly, mobile) broadband has likely become quite inelastic. In 2010, one study estimated that the typical household was willing to pay about $80 per month for “fast” broadband service, while the median monthly price for that service was about $40. Since then, the number of applications and available online services has increased, meaning that consumer willingness to pay has presumably also increased, while according to the Bureau of Labor Statistics broadband prices have remained about the same.

Source: Bureau of Labor Statistics, Series ID CUUR0000SEEE03, adjusted so January 2007=100

While no recent study has specifically evaluated price elasticity, the large gap between prices and willingness to pay suggests that a tax of any size likely to be considered might not be hugely inefficient overall.

The problem is that even if the tax does not affect subscription decisions by most people, it can still affect precisely the population policymakers want to help. Even though only 10 percent of people who do not have broadband cite price as the barrier, there is some lower price at which people will subscribe. A tax effectively increases the price consumers pay, meaning that it puts people at that margin—people who may be on the verge of subscribing—that much further away from deciding broadband is worthwhile. Similarly, people on the other side of that margin—those who believe broadband is worthwhile, but just barely—will either cancel their subscriptions or subscribe to less robust offerings.

To be sure, people who would be eligible for low-income support would probably receive more in subsidies than they pay in taxes, but this is an absurdly inefficient way to connect more people. As one astute observer noted, it is not merely like trying to fill a leaky bucket, but perhaps more like trying to fill that bucket upside-down through the holes.[1]

Higher prices for everyone highlights the equity problem. A connection tax is the same for everyone regardless of income, making the tax regressive. The tax becomes even more regressive because much of the payments go to rural residents regardless of their income while everyone pays regardless of their income, meaning the tax includes a transfer from the urban poor to the rural rich.

Even without an income test, methods exist to mitigate the equity problem. Unfortunately, the methods the FCC proposes are likely to undermine other policy goals. In particular, the FCC asks about the effects of taxing by tier of service, presumably with higher tiers of service paying more (paragraph 249). The FCC does not specifically mention equity in its discussion, but if higher income people are more likely to have faster connections then it would help mitigate equity issues.

This tiered tax approach is commonly used for other services, including electricity and water, where a low tier of use is taxed at a low rate, and higher usage rates are taxed incrementally more. Therefore, in the case of water, for example, a family that uses water only for cooking and cleaning will pay a lower tax rate than a family that also waters its lawn and fills a swimming pool. And while it is not a perfect measure of income, in general wealthier people are more likely to have big lawns and pools.

The problem with this approach in broadband is that while willingness to pay for “fast” broadband is relatively high, most people are not yet willing to pay much more at all for “very fast” broadband. Thus, taxing higher tiers of service at a higher price, while more equitable, may lead to other efficiency problems if it reduces demand for higher tiers of service.

So what’s the solution?

The FCC should decide which objectives are the most important: efficiency, equity, or other policy objectives such as inducing more people to subscribe or upgrade their speeds, and then design the tax system that best achieves that goal. Then it should compare this “best” tax to the outcome if the system were simply funded from general revenues and compare which of those would lead to a better outcome.

But no tax is worthwhile if the program it supports is itself inefficient and inequitable. The real solution is to dramatically reduce spending on ineffective universal service programs in order to minimize the amount of money needed to fund them. Unfortunately, the reforms appear to do just the opposite. In 2011, the high cost fund spent $4.03 billion and had been projected to decrease even further. The reforms, however, specified that spending should not fall below $4.5 billion (see paragraph 560 of the order), meaning that the first real effect of the reforms was to increase spending by a half billion dollars. And, as the GAO noted, the FCC “has not addressed its inability to determine the effect of the fund and lacks a specific data-analysis plan for carrier data it will collect” and “lacks a mechanism to link carrier rates and revenues with support payments.”

The right reforms include integrating true, third-party, evaluation mechanisms into the program and, given the vast evidence of inefficiency and ineffectiveness, a future path of steady and significant budget cuts. Those changes combined with an efficient tax-collection method, might yield a program that efficiently targets those truly in need.

Those who favor expanding the FTC’s role with respect to privacy should take a close look at what the agency does with the authority it already has. The most recent exhibit is the FTC’s imposition of a $22.5 million penalty on Google for bypassing the privacy settings on Apple’s Safari browser and thereby violating the terms of Google’s 2011 consent decree with the FTC. Since this is the largest fine the FTC has ever imposed, one would think Google must have committed a pretty serious violation that resulted in substantial harm to consumers. But there is no evidence that consumers have been harmed at all. (Dan Castro has written a nice blog post on this). Instead, the FTC has uncovered just enough of a technical violation to be able to say to Google “gotcha again.”

The issue is difficult to explain briefly, but essentially what happened is this: Google’s social network, Google +, has a “+1” button that, like Facebook’s “Like” button, gives users a way to indicate content they like. This feature doesn’t work with Apple’s Safari browser, which has a do-not-track feature that is turned on by default, so Google developed a tool that made the Safari browser work like other browsers.

Following research of a Stanford graduate student which was reported in the Wall Street Journal, the FTC began investigating and discovered two sentences in a 2009 Google help center page that the FTC claims misrepresent what Google is doing. That language dated from a year before Apple adopted its current cookie policy, two years before the 2011 consent decree, and two years before the +1 button was introduced.

Whether or not Google technically violated its consent decree, it is difficult to see how the Commission’s action will benefit consumers. Paradoxically, the action is likely to undermine one of the Commission’s principle recommendations: “greater transparency” concerning information collection and use practices. The $22 million fine sends exactly the opposite message to Google as well as other firms subject to FTC jurisdiction. The more transparent a company is about how it collects and uses data, the greater the risk of making a mistake and getting in trouble with the FTC. So, companies will find it in their interest to give users less information about web site privacy practices.

In addition, there is a cost to the +1 users the FTC is supposedly protecting. Now that Google has “corrected” the problem, Safari users who want to use +1 need to manually log in to their Google account, which equates to submitting a form, which then allows additional Google cookies to be installed anyway. This is quite a cumbersome process. Moreover, the pre-correction Google workaround meant that only additional cookies from Google’s Doubleclick network could be installed, while blocking cookies from any other third party. The current fix forces users who want to use the +1 function to change the cookie settings for the entire browser, opening their phones to cookies from any website, unless they take the trouble to switch settings back to ‘never accept’ cookies after they have successfully ‘+1′ the content they set out to share.

That FTC privacy-related enforcement is not based on demonstrable consumer benefits should not come as a surprise to those who have been following the agency’s work in this area. In the past two years, the Commission has released two privacy reports (here and here) that contain no evidence of consumer harm from current privacy practices. In fact, the Commission explicitly rejects the harm-based approach to privacy. This, of course, makes analysis of the benefits of proposed measures difficult, since if there are benefits they will consist of reduced harms.

So, what are the broader lessons from this episode? First, we should be wary of privacy legislation that gives the FTC additional authority to write new rules and enforce them (which virtually all privacy legislative proposals would do). If new legislation is enacted, it should only be with a strict mandate that any new regulations address significant harms and pass a cost-benefit test.

Another lesson may be for companies like Google, who understandably are anxious to avoid protracted litigation and get on with their businesses. These companies probably need to reassess the cost-benefit calculation that induced them to settle in the first place.

Missy Franklin, America’s newest star swimmer and most winning Olympian this summer, took home $110,000 in addition to her four gold and one bronze medals. That’s because the United States Olympic Committee pays athletes $25,000 for each gold, $15,000 for each silver, and $10,000 for each bronze medal they win. The United States isn’t alone in rewarding winning athletes. At least 42 other countries also pay winners, with payouts for gold ranging from about $11,000 in Kenya to $1.2 million in Georgia.

(click the picture to enlarge it)

Incentives might mean more than money. In 2008, medalists from Belarus received not just money, but also free meat for life.[1] At least one country includes a stick in addition to its incentive carrot. North Korea, which is apparently the basis for The Hunger Games, reportedly rewards its gold medal winners “with handsome prize money, an apartment, a car, and additional perks like refrigerators and television sets,” while “poor performances” could yield time in a labor camp.[2]

Why do countries pay winners? The most obvious answer is that they want to create an additional incentive to win. But does it? We set out to answer this question.

We put together data on each country’s medal count and medal payout scheme for the 2012 Olympics in order to test for a correlation.[3] We also control for country size and income, since those are surely the biggest predictor of how many medals a country will win: more populous countries are more likely to have that rare human who is physically built and mentally able to become an Olympic athlete, while richer countries are more likely to be able to invest in training those people.

Thus, we estimate the following regression:

Medalc = f(populationc, per capita incomec, medal payoutc), where c indicates the country. The table below shows the results. As expected, bigger and richer countries win more medals, especially gold medals. Curiously, however, medal payouts are negatively correlated with winning medals, and this correlation is statistically significant for bronze and silver medal payouts.

Table 1: Relationship Between the Number of Gold, Silver, and Bronze Medals and Country Characteristics

(including only countries for which we could find information)

What could explain this counterintuitive result?

One possibility is that it is simply wrong. Data on medal payouts do not exist in one place, so we had to search for information on each country. As a result, just because we could not find data on a country’s payouts does not mean it doesn’t have any. In the analysis above I excluded countries for which we had no information on medals. Most of the countries omitted probably did not offer rewards, but we cannot say this with certainty.

We can partially address this question by assuming—and I emphasize that this may be an inappropriate assumption—that countries for which we found no data have no payouts and re-estimating the regression. The table below shows these results. Under this assumption, the results change slightly. Gold and silver payouts become positively correlated with number of medals won, but are far from statistically significant. In other words, this analysis simply suggests that there is little, if any, correlation between awards and medals. Payouts for bronze medals, however, become positive and statistically significant.

Table 2: Relationship Between the Number of Gold, Silver, and Bronze Medals and Country Characteristics

(including all countries and assuming no information on rewards means country does not offer rewards)

Another complication is that this analysis does not take into account non-monetary payouts, such as meat-for-life, or punishments like banishment to labor camps, but we do not have a way at the moment to incorporate that information empirically.

Finding no correlation between monetary payments and medals is not surprising in some countries. In the United States, for example, $25,000—while perhaps today a sizable per-medal sum for high school student Missy Franklin—is dwarfed by the millions of dollars she can earn in endorsements now that she has won her medals.

Such valuable endorsement opportunities, however, are probably less common in other countries, while the relative value of the rewards might be much higher. So, for example, $11,000 in Kenya may provide a much stronger incentive than $25,000 in the United States. We test this hypothesis by running the original regression and excluding richer countries. I exclude the tables here, but the results do not change: even including only poorer countries, where the rewards are more likely to make a material difference in someone’s life, rewards are not significantly correlated with winning more medals, even controlling for population and income.

Another explanation for these results is that elite athletes likely compete for a host of reasons unrelated to money: because they love their sport, because they want to be the best, or for other personal reasons that give them satisfaction.

But why would payouts be negatively correlated with winning medals? My guess is that in reality they aren’t. Instead, the payouts proxy for something else. Perhaps countries that give larger monetary payouts perform worse than one would expect given their size and wealth, and hope that the payouts incent better performance by their athletes. In this case, payouts would be negatively correlated with a country’s performance, all else equal, not because they are not incentives, but because they are there because the country should be doing better.

Still, overall the evidence suggests that these payments don’t increase the medal count. And because they benefit such a small number of athletes it’s hard to argue that they are important for making it possible to make a living as an Olympic-level athlete.

Overall, if the desire is to create additional incentives to win, countries might want to re-think their reward system.

Note: I thank Corwin Rhyan and Anjney Midha for for their excellent research assistance and indulging me by hunting laboriously for data on the olympics even though we work at a think tank devoted to technology issues.